Carbon dioxide (CO2)

Databases used:

• Atmospheric C02 from the Muana Loa Observatory (ESRL at NOAA)

• Oceanic values from the Hawaii Ocean Time-series (HOT)

import os
import os.path as op

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as sp

import sys
sys.path.append("../../../functions")
from data_downloaders import download_HOT_CO2_data, download_MLO_CO2_data

sys.path.append("../../../../indicators_setup")
from ind_setup.plotting import plot_timeseries
from ind_setup.plotting_int import plot_timeseries_interactive
from ind_setup.tables import plot_df_table

Observations from NOAA

update_data = True
path_data = "../../../data"

url = 'https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_mlo.txt'

if update_data:
    MLO_data = download_MLO_CO2_data(url)
    MLO_data.to_pickle(op.join(path_data, 'MLO_co2.pkl'))
else:
    MLO_data = pd.read_pickle(op.join(path_data, 'MLO_co2.pkl'))    

Observations from U.Hawaii

Plotting

Muana Loa Observatory

dict_plot = [{'data' : MLO_data, 'var' : 'CO2', 'ax' : 1, 'label' : 'MLO: CO2'},]

MLO_data['CO2_deseasoned'] = MLO_data['CO2'].rolling(window=12, center=True).mean()

dict_plot = [{'data' : MLO_data, 'var' : 'CO2', 'ax' : 1, 'trendline' : True, 'label' : 'MLO: CO2'},
             {'data' : MLO_data, 'var' : 'CO2_deseasoned', 'ax' : 1, 'trendline' : False, 'label' : 'MLO: CO2 (Deseasoned)'}]

plot_timeseries_interactive(dict_plot, trendline = False, figsize = (20, 12));

plot_timeseries_interactive(dict_plot, trendline = True, figsize = (20, 10));

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from scipy.stats import linregress
MLOy = MLO_data[['CO2']].groupby(MLO_data.index.year).mean()
MLOy.index = pd.to_datetime(MLOy.index, format = '%Y')
slope, intercept, r_value, p_value, std_err = linregress(MLOy.index.year, MLOy['CO2'])

Annual Stats

d = MLOy.describe()
d.loc['Rate of change'] = slope
fig = plot_df_table(np.round(d, 2), figsize = (400, 400))